Determining Descriptive Attributes for Listing Locations

Abstract

Listings and reviews of listings can be processed to identify descriptive attributes for locations associated with the listings. To do this, a corpus of words is generated for various locations based on listings in the locations and reviews of those listings. An expected frequency, and per-location frequency for each word is determined. These numbers are in turn used to determine a number of high frequency listing locations, and a number of below expected frequency listing locations for each word. Based on a comparison of the number of high frequency listing locations and the number of below expected frequency listing locations of a word with an attribute reference number, the word can be identified either as an attribute that is likely descriptive of the location, or not.

What is claimed is: 
     
         1 . A method comprising:
 generating a corpus of words present in listings and reviews of the listings, the listings describing goods or services, each listing associated with a location of a plurality of locations;   for each of the words in the corpus:
 computing an expected frequency for a word to appear in the corpus, 
 determining, for each of the locations, a per-location frequency for the word, 
 determining a number of high frequency listing locations comprising locations where the per-location frequency of the word is a first multiple greater than the expected frequency, 
 determining a number of low frequency listing locations comprising locations where the per-location frequency of the word is a second multiple smaller than the expected frequency, and 
 determining a descriptiveness metric for the word based on the number of high frequency listings locations and the number of low frequency listing locations; 
   identifying, as attributes, one or more words in the set of words having a descriptiveness metric within a threshold range of an attribute reference number;   generating for display a graphical user interface (GUI) comprising a search query bar and icons for each of a default subset of locations of the plurality of locations;   receiving, via the search query bar of the GUI, a user search query specifying an attribute of interest;   determining an updated subset of the plurality of locations, the updated subset comprising locations of the plurality of locations that have the attribute of interest as an identified attribute; and   updating the GUI to replace the icons for each of the default subset of locations with icons for each of the updated subset of the plurality of locations, the icons including a display of, for each respective location of the updated subset, the respective one or more words having the descriptiveness metric within the threshold range of the attribute reference number.   
     
     
         2 . The method of  claim 1 , wherein the expected frequency is based on a total number of times the word occurs in the corpus and a total number of words in the corpus. 
     
     
         3 . The method of  claim 1 , wherein the per-location frequency based on a total number of times the word occurs in listings associated with the location. 
     
     
         4 . The method of  claim 1 , wherein the descriptiveness metric is a ratio of the number of high frequency listings locations to the number of low frequency listing locations. 
     
     
         5 . The method of  claim 1 , wherein the descriptiveness metric is a numerical value that represents how descriptive a word is of a location relative to the other words in the corpus. 
     
     
         6 . The method of  claim 1 , wherein the attribute reference number is 1. 
     
     
         7 . The method of  claim 1 , wherein the words in the corpus comprise bigrams and trigrams. 
     
     
         8 . The method of  claim 1 , wherein the expected frequency is based on a total number of times the word occurs in the corpus, a total number of times other words semantically similar to the word occur in the corpus, and a total number of words in the corpus. 
     
     
         9 . The method of  claim 1 , further comprising:
 receiving a request for attributes of one of the locations;   identifying a subset of the corpus comprising words present in listings and reviews of the listings associated with the location;   comparing the attributes against the subset of words to determine a list of attributes for the location; and   providing the list of attributes for the location in response to the request.   
     
     
         10 . The method of  claim 9 , wherein comparing the attributes against the subset of words to determine the list of attributes for the location comprises:
 identifying which of the attributes are present as words in the subset of the corpus.   
     
     
         11 . A non-transitory computer readable storage medium comprising instructions that when executed by at least one processor cause the processor to:
 generate a corpus of words present in listings and reviews of the listings, the listings describing goods or services, each listing associated with a location of a plurality of locations;   for each of the words in the corpus:
 compute an expected frequency for a word to appear in the corpus, 
 determine, for each of the locations, a per-location frequency for the word, 
 determine a number of high frequency listing locations comprising locations where the per-location frequency of the word is a first multiple greater than the expected frequency, 
 determine a number of low frequency listing locations comprising locations where the per-location frequency of the word is a second multiple smaller than the expected frequency, and 
 determine a descriptiveness metric for the word based on the number of high frequency listings locations and the number of low frequency listing locations; 
   identify, as attributes, one or more words in the set of words having a descriptiveness metric within a threshold range of an attribute reference number;   generate for display a graphical user interface (GUI) comprising a search query bar and icons for each of a default subset of locations of the plurality of locations;   receive, via the search query bar of the GUI, a user search query specifying an attribute of interest;   determine an updated subset of the plurality of locations, the updated subset comprising locations of the plurality of locations that have the attribute of interest as an identified attribute; and   update the GUI to replace the icons for each of the default subset of locations with icons for each of the updated subset of the plurality of locations, the icons including a display of, for each respective location of the updated subset, the respective one or more words having the descriptiveness metric within the threshold range of the attribute reference number   
     
     
         12 . The non-transitory computer readable storage medium of  claim 11 , wherein the expected frequency is based on a total number of times the word occurs in the corpus and a total number of words in the corpus. 
     
     
         13 . The non-transitory computer readable storage medium of  claim 11 , wherein the per-location frequency based on a total number of times the word occurs in listings associated with the location. 
     
     
         14 . The non-transitory computer readable storage medium of  claim 11 , wherein the descriptiveness metric is a ratio of the number of high frequency listings locations to the number of low frequency listing locations. 
     
     
         15 . The non-transitory computer readable storage medium of  claim 11 , wherein the descriptiveness metric is a numerical value that represents how descriptive a word is of a location relative to the other words in the corpus. 
     
     
         16 . The non-transitory computer readable storage medium of  claim 11 , wherein the attribute reference number is 1. 
     
     
         17 . The non-transitory computer readable storage medium of  claim 11 , wherein the words in the corpus comprise bigrams and trigrams. 
     
     
         18 . The non-transitory computer readable storage medium of  claim 11 , wherein the expected frequency is based on a total number of times the word occurs in the corpus, a total number of times other words semantically similar to the word occur in the corpus, and a total number of words in the corpus. 
     
     
         19 . The non-transitory computer readable storage medium of  claim 11 , wherein the instructions further cause the processor to:
 receive a request for attributes of one of the locations;   identify a subset of the corpus comprising words present in listings and reviews of the listings associated with the location;   compare the attributes against the subset of words to determine a list of attributes for the location; and   provide the list of attributes for the location in response to the request.   
     
     
         20 . The non-transitory computer readable storage medium of  claim 19 , wherein the instructions further cause the at least one processor, when comparing the attributes against the subset of words to determine the list of attributes for the location, to:
 identify which of the attributes are present as words in the subset of the corpus.